geo-optimizer-mcp
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- AlicenseNot gradedqualityBmaintenanceEnables auditing any website for AI-answer readiness, returning a score and actionable fix list based on llms.txt, schema, sitemap, and related checks.MIT
- AlicenseNot gradedqualityCmaintenanceEnables auditing websites for AI/LLM readiness and generating fixes such as robots.txt rules, llms.txt, JSON-LD schema, and meta tags, plus checking brand and domain citations through natural language.1MIT
- AlicenseNot gradedqualityCmaintenanceGenerates a complete suite of AI readiness files (llms.txt, ai.txt, schema, RAG indexes) for any website to optimize representation in ChatGPT, Claude, Gemini, and Perplexity.24 npm3MIT
- AlicenseAqualityBmaintenanceEnables AI agents to check whether a public website is crawlable, understandable, and ready for AI search workflows through local-only audits of robots.txt, sitemaps, metadata, and llms.txt.371 npm1MIT

agentbuiltofficial
AlicenseNot gradedqualityCmaintenanceEnables AI agents to run a free AI-readiness audit of any URL, checking AI crawler rules, JavaScript-free page text, JSON-LD, llms.txt, sitemap, meta description, FAQ schema, and returning a score with findings and fixes. Also exposes the same capability via an A2A agent endpoint.MIT- AlicenseNot gradedqualityBmaintenanceEnables users to audit websites and online stores for generative and answer-engine readiness, generate deployable llms.txt and llms-full.txt files, JSON-LD schema, and AI crawler rules, analyze robots access for AI bots, and extract clean page content for LLM use.MIT
TDQS
Scored across 6 tools
Tool triggers and workflows are mostly distinct, with explicit cross-references (analyze then get_geo_fixes; guide first). However, get_geo_fixes and make_site_geo_ready produce overlapping artifacts (schema, llms.txt, robots.txt) and could be confused for existing vs. rebuilding sites, and analyze_geo_score overlaps with get_site_report on score reporting.
All tools use consistent snake_case with a verb-first pattern (analyze_, get_, make_, suggest_), making the set predictable and readable.
Six tools form a well-scoped set for a GEO optimization assistant; each tool covers a distinct phase (guide, audit, fixes, asset generation, monitoring config, reporting) without redundant entries.
The surface covers audit, remediation guidance, asset generation, monitoring suggestions, and reporting. Minor gaps remain: no tool to apply or validate generated assets, and no direct management of monitoring configs after suggestion.